Implementation of a Best Practice Advisory to Improve Infection Screening Prior to New Prescriptions of Biologics and Targeted Synthetic Drugs.

OBJECTIVE: Use of biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in patients with preexisting tuberculosis (TB), hepatitis B virus (HBV), or hepatitis C virus (HCV) infection can have serious consequences. Although various society guidelines recommend routine screening for these infections before initiating certain b/tsDMARDs, adherence to these recommendations varies widely. This quality improvement initiative evaluated local compliance with screening and assessed whether an automated computerized decision support system in the form of a best practice advisory (BPA) in the electronic health record could improve patient screening. METHODS: Established patients with autoimmune rheumatic disease (ARD) aged 18 years or older with at least one visit to our rheumatology practice between October 1, 2017, and March 3, 2022, were included. When prescribing a new b/tsDMARD, clinicians were alerted via a BPA that showed the most recent results for TB, HBV, and HCV. Screening proportions for TB, HBV, and HCV before BPA initiation were compared with those of eligible patients after the BPA implementation. RESULTS: A total of 711 patients pre-BPA and 257 patients post-BPA implementation were included in the study. The BPA implementation was associated with statistically significant improvement in screening for TB from 66% to 82% (P ≤ 0.001), HCV from 60% to 79% (P ≤ 0.001), hepatitis B core antibody 32% to 51% (P ≤ 0.001), and hepatitis B surface antigen from 51% to 70% (P ≤ 0.001). CONCLUSION: Implementation of a BPA can improve infectious disease screening for patients with ARD who are started on b/tsDMARDs and has potential to improve patient safety.

Accuracy of the Veterans Health Administration COVID-19 (VACO) Index for predicting short-term mortality among 1307 US academic medical centre inpatients and 427 224 US Medicare patients.

BACKGROUND: The Veterans Health Administration COVID-19 (VACO) Index predicts 30-day all-cause mortality in patients with COVID-19 using age, sex and pre-existing comorbidity diagnoses. The VACO Index was initially developed and validated in a nationwide cohort of US veterans-we now assess its accuracy in an academic medical centre and a nationwide US Medicare cohort. METHODS: With measures and weights previously derived and validated in US national Veterans Health Administration (VA) inpatients and outpatients (n=13 323), we evaluated the accuracy of the VACO Index for estimating 30-day all-cause mortality using area under the receiver operating characteristic curve (AUC) and calibration plots of predicted versus observed mortality in inpatients at a single US academic medical centre (n=1307) and in Medicare inpatients and outpatients aged 65+ (n=427 224). RESULTS: 30-day mortality varied by data source: VA 8.5%, academic medical centre 17.5%, Medicare 16.0%. The VACO Index demonstrated similar discrimination in VA (AUC=0.82) and academic medical centre inpatient population (AUC=0.80), and when restricted to patients aged 65+ in VA (AUC=0.69) and Medicare inpatient and outpatient data (AUC=0.67). The Index modestly overestimated risk in VA and Medicare data and underestimated risk in Yale New Haven Hospital data. CONCLUSIONS: The VACO Index estimates risk of short-term mortality across a wide variety of patients with COVID-19 using data available prior to or at the time of diagnosis. The VACO Index could help inform primary and booster vaccination prioritisation, and indicate who among outpatients testing positive for SARS-CoV-2 should receive greater clinical attention or scarce treatments.

Feasibility of establishing a multi-center research database using the electronic health record: The PURSUIT network.

BACKGROUND: Although multi-center research is needed in pediatric urology, collaboration is impeded by differences in physician documentation and research resources. Electronic health record (EHR) tools offer a promising avenue to overcome these barriers. OBJECTIVE: To assess the accuracy, completeness, and utilization of structured data elements across multiple practices. STUDY DESIGN: A standardized template was developed and implemented at five academic pediatric urology practices to document clinic visits for patients with congenital hydronephrosis and/or vesicoureteral reflux. Data from standardized elements in the template and from pre-existing EHR fields were extracted into a secure database. A 20% random sample of infants with data from structured elements from 1/1/2020 and 4/30/2021 were identified and compared to manual chart review at sites with >100 charts; all other sites reviewed at least 20 charts. Manual chart review was standardized across sites and included: clinic and operative notes, orders linked to the clinic encounter, radiology results, and active medications. Accuracy of data extraction was evaluated by computing the kappa statistic and percentage agreement. For sites that had adopted the templates prior to 6/1/2019 (early adopters), a list of eligible patients with an initial clinic visit from 1/1/2020-7/27/2020 was generated using standardized reporting techniques and confirmed by manual chart review. Physician utilization of the template was then calculated by comparing patients with data obtained from the note template to the generated list of eligible patients. RESULTS: 230 patient records met study criteria. Agreement between manual chart review and data extracted from the EHR was high (>85%). Race, ethnicity and insurance data were misclassified in about 10-15% of cases; this was due to site-specific differences in how these fields were coded. Renal ultrasound was misclassified 12% of the time; this was primarily due to outside images documented in radiology results but not included in the clinical note. All other data elements had >90% agreement (Figure). Template utilization for early adopters was >75% (75.5-87.5%). DISCUSSION: This is the first study in urology to demonstrate that use of structured data elements can support multi-center research. Limitations include: inclusion of only academic sites with the Epic EHR and lack of data on utilization and sustainability at sites without a prior history of structured template use. CONCLUSIONS: Multi-center research collaboration using EHR-based data collection tools is feasible with generally high accuracy compared to manual chart review. Additionally, sites with a long history of template adoption have high levels of provider utilization.